Bonjour, je suis Thomas, lead engineer chez HolySheep AI. Aujourd'hui, je partage notre parcours complet d'intégration du protocole MCP (Model Context Protocol) avec notre infrastructure, en incluant les pièges que nous avons rencontrés lors de la validation des schémas tool-use et comment nous les avons résolus.
Contexte et motivation économique
En 2026, les coûts d'inférence ont atteint des niveaux où chaque milliseconde compte. Voici notre comparaison mensuelle pour un volume de 10 millions de tokens :
| Modèle | Prix output ($/MTok) | Coût 10M tokens/mois | Latence typique |
|---|---|---|---|
| GPT-4.1 | 8,00 $ | 80 $ | ~180 ms |
| Claude Sonnet 4.5 | 15,00 $ | 150 $ | ~220 ms |
| Gemini 2.5 Flash | 2,50 $ | 25 $ | ~85 ms |
| DeepSeek V3.2 | 0,42 $ | 4,20 $ | ~95 ms |
| HolySheep DeepSeek V3.2 | 0,42 ¥ (≈0,42 $) | 4,20 ¥ | <50 ms |
Avec HolySheep, grâce au taux de change avantageux (1 ¥ = 1 $) et à notre infrastructure optimisée, nous offrons une économie de 85%+ par rapport aux tarifs officiels US, tout en bénéficiant d'une latence inférieure à 50 ms. Si vous souhaitez tester notre plateforme, inscrivez-vous ici et recevez des crédits gratuits.
Qu'est-ce que le MCP et pourquoi l'intégrer ?
Le Model Context Protocol (MCP) est un standard ouvert qui permet aux modèles d'IA d'interagir avec des outils externes de manière standardisée. Pour HolySheep, l'intégration MCP nous permet de :
- Fournir un point d'entrée unique compatible avec les outils Anthropic
- Valider les schémas tool-use côté client
- Réduire les coûts en interceptant les appels avant qu'ils n'atteignent l'API
- Offrir une expérience developer cohérente entre providers
Architecture de notre solution MCP
Notre implémentation repose sur trois composants principaux :
- MCP Proxy Server : Traduit les appels Anthropic vers HolySheep
- Schema Validator : Valide les tool_calls avant exécution
- Cost Optimizer : Analyse et optimise les appels
Implémentation du client MCP avec HolySheep
Voici notre implémentation complète du client MCP qui se connecte à HolySheep :
"""
HolySheep MCP Client - Intégration avec validation tool-use
Auteur: Thomas, Lead Engineer HolySheep AI
Version: 2.1649
"""
import httpx
import json
import asyncio
from typing import List, Optional, Dict, Any
from dataclasses import dataclass, field
from datetime import datetime
import jsonschema
from jsonschema import Draft7Validator
@dataclass
class ToolCall:
"""Représente un appel d'outil avec validation."""
name: str
arguments: Dict[str, Any]
schema: Optional[Dict[str, Any]] = None
def validate(self) -> List[str]:
"""Valide les arguments contre le schéma."""
if not self.schema:
return []
errors = []
validator = Draft7Validator(self.schema)
for error in validator.iter_errors(self.arguments):
path = ".".join(str(p) for p in error.path)
errors.append(f"{path}: {error.message}")
return errors
@dataclass
class MCPMessage:
"""Message MCP formaté."""
role: str
content: str
tool_calls: Optional[List[ToolCall]] = None
tool_call_id: Optional[str] = None
timestamp: datetime = field(default_factory=datetime.utcnow)
class HolySheepMCPClient:
"""Client MCP pour HolySheep API avec validation."""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(
self,
api_key: str,
model: str = "deepseek-v3.2",
timeout: float = 30.0
):
self.api_key = api_key
self.model = model
self.timeout = timeout
self.tools_registry: Dict[str, Dict[str, Any]] = {}
self.call_history: List[Dict[str, Any]] = []
self.client = httpx.AsyncClient(
timeout=httpx.Timeout(timeout),
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"X-MCP-Version": "2026-05-16"
}
)
def register_tool(
self,
name: str,
description: str,
input_schema: Dict[str, Any]
) -> None:
"""Enregistre un nouvel outil avec son schéma."""
self.tools_registry[name] = {
"name": name,
"description": description,
"input_schema": input_schema
}
async def chat_completion(
self,
messages: List[Dict[str, Any]],
tools: Optional[List[Dict[str, Any]]] = None,
temperature: float = 0.7,
max_tokens: int = 4096
) -> Dict[str, Any]:
"""Envoie une requête de chat completion avec outils."""
# Construire le payload MCP-compatible
payload = {
"model": self.model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
"stream": False
}
# Ajouter les outils si fournis
if tools:
payload["tools"] = tools
try:
response = await self.client.post(
f"{self.BASE_URL}/chat/completions",
json=payload
)
response.raise_for_status()
result = response.json()
# Enregistrer l'appel pour analyse
self._record_call(messages, payload, result)
return self._parse_mcp_response(result)
except httpx.HTTPStatusError as e:
return {
"error": True,
"status_code": e.response.status_code,
"message": self._parse_error(e.response),
"retryable": e.response.status_code in [429, 500, 502, 503]
}
except Exception as e:
return {
"error": True,
"message": str(e),
"retryable": False
}
def _record_call(
self,
messages: List[Dict],
request: Dict,
response: Dict
) -> None:
"""Enregistre l'appel pour l'analyse de coûts."""
usage = response.get("usage", {})
self.call_history.append({
"timestamp": datetime.utcnow().isoformat(),
"model": self.model,
"input_tokens": usage.get("prompt_tokens", 0),
"output_tokens": usage.get("completion_tokens", 0),
"total_tokens": usage.get("total_tokens", 0),
"cost_usd": self._calculate_cost(usage)
})
def _calculate_cost(self, usage: Dict) -> float:
"""Calcule le coût en USD."""
rates = {
"deepseek-v3.2": 0.42, # ¥/MTok = $/MTok
"claude-sonnet-4.5": 15.0,
"gpt-4.1": 8.0,
"gemini-2.5-flash": 2.5
}
rate = rates.get(self.model, 0.42)
total_mtok = usage.get("total_tokens", 0) / 1_000_000
return round(total_mtok * rate, 6)
def _parse_mcp_response(self, response: Dict) -> Dict[str, Any]:
"""Parse la réponse au format MCP."""
choices = response.get("choices", [{}])
choice = choices[0] if choices else {}
return {
"error": False,
"role": choice.get("message", {}).get("role", "assistant"),
"content": choice.get("message", {}).get("content", ""),
"tool_calls": self._extract_tool_calls(
choice.get("message", {}).get("tool_calls", [])
),
"usage": response.get("usage", {}),
"model": response.get("model", self.model),
"latency_ms": response.get("latency_ms", 0)
}
def _extract_tool_calls(
self,
raw_calls: List[Dict]
) -> List[ToolCall]:
"""Extrait et valide les tool calls."""
validated_calls = []
for call in raw_calls:
tool_name = call.get("function", {}).get("name", "unknown")
args = json.loads(call.get("function", {}).get("arguments", "{}"))
tool_call = ToolCall(
name=tool_name,
arguments=args,
schema=self.tools_registry.get(tool_name, {}).get("input_schema")
)
errors = tool_call.validate()
if errors:
tool_call.validation_errors = errors
validated_calls.append(tool_call)
return validated_calls
def _parse_error(self, response: httpx.Response) -> str:
"""Parse le message d'erreur."""
try:
data = response.json()
return data.get("error", {}).get("message", data.get("message", "Unknown error"))
except:
return response.text
async def execute_tool(
self,
tool_call: ToolCall
) -> Dict[str, Any]:
"""Exécute un tool call validé."""
# Validation finale
errors = tool_call.validate()
if errors:
return {
"error": True,
"message": "Validation failed",
"details": errors
}
# Exécuter selon le type d'outil
handlers = {
"search": self._handle_search,
"database": self._handle_database,
"http": self._handle_http,
"code": self._handle_code_execution
}
handler = handlers.get(tool_call.name.split("_")[0])
if handler:
return await handler(tool_call.arguments)
return {"error": True, "message": f"Unknown tool: {tool_call.name}"}
async def _handle_search(self, args: Dict) -> Dict:
"""Handler pour recherche."""
query = args.get("query", "")
limit = args.get("limit", 10)
return {"results": [], "query": query, "count": 0}
async def _handle_database(self, args: Dict) -> Dict:
"""Handler pour base de données."""
return {"rows": [], "affected": 0}
async def _handle_http(self, args: Dict) -> Dict:
"""Handler pour requêtes HTTP."""
return {"status": 200, "body": ""}
async def _handle_code_execution(self, args: Dict) -> Dict:
"""Handler pour exécution de code."""
code = args.get("code", "")
language = args.get("language", "python")
return {"output": "", "execution_time_ms": 0}
def get_cost_report(self) -> Dict[str, Any]:
"""Génère un rapport de coûts."""
total_input = sum(c["input_tokens"] for c in self.call_history)
total_output = sum(c["output_tokens"] for c in self.call_history)
total_cost = sum(c["cost_usd"] for c in self.call_history)
return {
"period": {
"start": self.call_history[0]["timestamp"] if self.call_history else None,
"end": self.call_history[-1]["timestamp"] if self.call_history else None
},
"total_calls": len(self.call_history),
"tokens": {
"input": total_input,
"output": total_output,
"total": total_input + total_output
},
"cost_usd": round(total_cost, 2),
"cost_cny": round(total_cost, 2), # 1:1 avec HolySheep
"average_latency_ms": 0
}
async def close(self):
"""Ferme le client."""
await self.client.aclose()
Exemple d'utilisation
async def main():
client = HolySheepMCPClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
model="deepseek-v3.2"
)
# Définir les outils disponibles
tools = [
{
"type": "function",
"function": {
"name": "search",
"description": "Recherche dans une base de connaissances",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"minLength": 3,
"maxLength": 500
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 100,
"default": 10
},
"filters": {
"type": "object",
"properties": {
"category": {"type": "string"},
"date_from": {"type": "string", "format": "date"},
"date_to": {"type": "string", "format": "date"}
}
}
},
"required": ["query"]
}
}
}
]
# Envoyer une requête
messages = [
{"role": "system", "content": "Tu es un assistant MCP."},
{"role": "user", "content": "Recherche les derniers articles sur MCP"}
]
result = await client.chat_completion(
messages=messages,
tools=tools
)
print(f"Response: {result['content']}")
print(f"Cost: ${result['usage']}")
# Générer le rapport
report = client.get_cost_report()
print(f"Total cost: {report['cost_usd']} USD")
await client.close()
if __name__ == "__main__":
asyncio.run(main())Validation des schémas tool-use : notre approche
La validation des schémas est critique pour éviter les erreurs runtime coûteuses. Voici notre module de validation avancé :
"""
Module de validation des schémas tool-use MCP
Inclut validation asynchrone, cache, et métriques
"""
import asyncio
import hashlib
import time
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
import json
from collections import defaultdict
class ValidationLevel(Enum):
"""Niveaux de validation."""
NONE = "none"
SYNTAX = "syntax"
SEMANTIC = "semantic"
FULL = "full"
@dataclass
class ValidationResult:
"""Résultat de validation."""
is_valid: bool
errors: List[str] = field(default_factory=list)
warnings: List[str] = field(default_factory=list)
duration_ms: float = 0.0
cache_hit: bool = False
@dataclass
class SchemaMetrics:
"""Métriques de validation."""
total_validations: int = 0
successful: int = 0
failed: int = 0
cache_hits: int = 0
average_duration_ms: float = 0.0
errors_by_type: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
class ToolUseValidator:
"""Validateur avancé pour les schémas tool-use."""
def __init__(self, cache_size: int = 1000):
self.cache: Dict[str, ValidationResult] = {}
self.cache_size = cache_size
self.metrics = SchemaMetrics()
self._lock = asyncio.Lock()
def _generate_cache_key(
self,
tool_name: str,
arguments: Dict[str, Any]
) -> str:
"""Génère une clé de cache unique."""
content = json.dumps({
"tool": tool_name,
"args": arguments
}, sort_keys=True)
return hashlib.sha256(content.encode()).hexdigest()[:16]
async def validate(
self,
tool_name: str,
arguments: Dict[str, Any],
schema: Dict[str, Any],
level: ValidationLevel = ValidationLevel.FULL
) -> ValidationResult:
"""Valide les arguments contre le schéma."""
start_time = time.perf_counter()
# Vérifier le cache
cache_key = self._generate_cache_key(tool_name, arguments)
async with self._lock:
if cache_key in self.cache:
cached = self.cache[cache_key]
cached.cache_hit = True
self.metrics.cache_hits += 1
return cached
# Validation selon le niveau
errors = []
warnings = []
if level in [ValidationLevel.SYNTAX, ValidationLevel.SEMANTIC, ValidationLevel.FULL]:
errors.extend(self._validate_types(arguments, schema))
errors.extend(self._validate_required(arguments, schema))
if level in [ValidationLevel.SEMANTIC, ValidationLevel.FULL]:
errors.extend(self._validate_constraints(arguments, schema))
warnings.extend(self._check_recommendations(arguments, schema))
if level == ValidationLevel.FULL:
errors.extend(await self._validate_semantics(
tool_name,
arguments,
schema
))
duration_ms = (time.perf_counter() - start_time) * 1000
result = ValidationResult(
is_valid=len(errors) == 0,
errors=errors,
warnings=warnings,
duration_ms=round(duration_ms, 3)
)
# Mettre en cache
async with self._lock:
if len(self.cache) >= self.cache_size:
# FIFO simple
first_key = next(iter(self.cache))
del self.cache[first_key]
self.cache[cache_key] = result
# Mettre à jour les métriques
self._update_metrics(result, errors)
return result
def _validate_types(
self,
arguments: Dict[str, Any],
schema: Dict[str, Any]
) -> List[str]:
"""Valide les types des arguments."""
errors = []
properties = schema.get("properties", {})
type_map = {
"string": str,
"number": (int, float),
"integer": int,
"boolean": bool,
"array": list,
"object": dict,
"null": type(None)
}
for key, value in arguments.items():
if key in properties:
expected_type = properties[key].get("type")
if expected_type and expected_type in type_map:
expected = type_map[expected_type]
if not isinstance(value, expected):
errors.append(
f"Argument '{key}': expected {expected_type}, "
f"got {type(value).__name__}"
)
return errors
def _validate_required(
self,
arguments: Dict[str, Any],
schema: Dict[str, Any]
) -> List[str]:
"""Valide les champs requis."""
errors = []
required = schema.get("required", [])
for field_name in required:
if field_name not in arguments:
errors.append(f"Missing required field: '{field_name}'")
elif arguments[field_name] is None:
errors.append(f"Field '{field_name}' cannot be null")
return errors
def _validate_constraints(
self,
arguments: Dict[str, Any],
schema: Dict[str, Any]
) -> List[str]:
"""Valide les contraintes (min/max, pattern, etc.)."""
errors = []
properties = schema.get("properties", {})
for key, value in arguments.items():
if key not in properties:
continue
prop = properties[key]
# Contraintes numériques
if isinstance(value, (int, float)):
if "minimum" in prop and value < prop["minimum"]:
errors.append(f"{key}: {value} < minimum ({prop['minimum']})")
if "maximum" in prop and value > prop["maximum"]:
errors.append(f"{key}: {value} > maximum ({prop['maximum']})")
# Contraintes de longueur
if isinstance(value, str):
if "minLength" in prop and len(value) < prop["minLength"]:
errors.append(f"{key}: length {len(value)} < minLength ({prop['minLength']})")
if "maxLength" in prop and len(value) > prop["maxLength"]:
errors.append(f"{key}: length {len(value)} > maxLength ({prop['maxLength']})")
if "pattern" in prop:
import re
if not re.match(prop["pattern"], value):
errors.append(f"{key}: does not match pattern '{prop['pattern']}'")
# Contraintes de tableau
if isinstance(value, list):
if "minItems" in prop and len(value) < prop["minItems"]:
errors.append(f"{key}: {len(value)} items < minItems ({prop['minItems']})")
if "maxItems" in prop and len(value) > prop["maxItems"]:
errors.append(f"{key}: {len(value)} items > maxItems ({prop['maxItems']})")
if "uniqueItems" in prop and prop["uniqueItems"] and len(value) != len(set(str(v) for v in value)):
errors.append(f"{key}: items must be unique")
return errors
def _check_recommendations(
self,
arguments: Dict[str, Any],
schema: Dict[str, Any]
) -> List[str]:
"""Génère des avertissements recommandés."""
warnings = []
properties = schema.get("properties", {})
for key, value in arguments.items():
if key not in properties:
continue
prop = properties[key]
# Valeur par défaut suggérée
if value is None and "default" in prop:
warnings.append(f"{key}: consider using default value '{prop['default']}'")
# Format suggéré
if isinstance(value, str) and "format" in prop:
if prop["format"] == "email" and "@" not in value:
warnings.append(f"{key}: invalid email format")
elif prop["format"] == "uri" and not value.startswith(("http://", "https://")):
warnings.append(f"{key}: invalid URI format")
return warnings
async def _validate_semantics(
self,
tool_name: str,
arguments: Dict[str, Any],
schema: Dict[str, Any]
) -> List[str]:
"""Validation sémantique asynchrone."""
errors = []
# Ajouter des validations sémantiques spécifiques ici
# Par exemple, vérifier des dépendances entre champs
if tool_name == "database_query":
if "table" in arguments and "filters" in arguments:
if not arguments["filters"] and arguments.get("mode") == "strict":
errors.append("Strict mode requires at least one filter")
return errors
def _update_metrics(
self,
result: ValidationResult,
errors: List[str]
) -> None:
"""Met à jour les métriques."""
self.metrics.total_validations += 1
if result.is_valid:
self.metrics.successful += 1
else:
self.metrics.failed += 1
for error in errors[:3]: # Limiter les types d'erreurs tracked
error_type = error.split(":")[0] if ":" in error else "unknown"
self.metrics.errors_by_type[error_type] += 1
# Moyenne mobile pour la durée
n = self.metrics.total_validations
current_avg = self.metrics.average_duration_ms
self.metrics.average_duration_ms = (
(current_avg * (n - 1) + result.duration_ms) / n
)
def get_metrics(self) -> Dict[str, Any]:
"""Retourne les métriques actuelles."""
return {
"total_validations": self.metrics.total_validations,
"success_rate": (
self.metrics.successful / self.metrics.total_validations * 100
if self.metrics.total_validations > 0 else 0
),
"cache_hit_rate": (
self.metrics.cache_hits / self.metrics.total_validations * 100
if self.metrics.total_validations > 0 else 0
),
"average_duration_ms": round(self.metrics.average_duration_ms, 3),
"top_errors": dict(
sorted(
self.metrics.errors_by_type.items(),
key=lambda x: x[1],
reverse=True
)[:5]
)
}
async def batch_validate(
self,
items: List[Dict[str, Any]],
schema: Dict[str, Any],
level: ValidationLevel = ValidationLevel.FULL,
max_concurrent: int = 10
) -> List[ValidationResult]:
"""Valide plusieurs items en parallèle."""
semaphore = asyncio.Semaphore(max_concurrent)
async def validate_one(item: Dict[str, Any]) -> ValidationResult:
async with semaphore:
return await self.validate(
tool_name=item["name"],
arguments=item["arguments"],
schema=schema,
level=level
)
return await asyncio.gather(*[validate_one(item) for item in items])
Exemple d'utilisation
async def demo_validation():
validator = ToolUseValidator(cache_size=500)
schema = {
"type": "object",
"properties": {
"query": {
"type": "string",
"minLength": 3,
"maxLength": 500
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 100,
"default": 10
},
"filters": {
"type": "object",
"properties": {
"category": {"type": "string"},
"date_range": {
"type": "object",
"properties": {
"from": {"type": "string"},
"to": {"type": "string"}
}
}
}
}
},
"required": ["query"]
}
# Tests de validation
test_cases = [
{"name": "search", "arguments": {"query": "MCP protocol", "limit": 5}},
{"name": "search", "arguments": {"query": "AB", "limit": 200}}, # Devrait échouer
{"name": "search", "arguments": {"limit": 10}}, # Devrait échouer (query manquant)
]
results = await validator.batch_validate(
test_cases,
schema,
level=ValidationLevel.FULL
)
for item, result in zip(test_cases, results):
print(f"\n{item['arguments']}:")
print(f" Valid: {result.is_valid}")
if result.errors:
print(f" Errors: {result.errors}")
if result.warnings:
print(f" Warnings: {result.warnings}")
print(f" Duration: {result.duration_ms}ms")
print(f" Cache hit: {result.cache_hit}")
print(f"\nMétriques: {validator.get_metrics()}")
if __name__ == "__main__":
asyncio.run(demo_validation())Erreurs courantes et solutions
Durant notre intégration MCP, nous avons rencontré plusieurs erreurs critiques. Voici les trois principales avec leurs solutions.
Erreur 1 : Échec de validation du schéma avec "Unexpected property"
# ❌ ERREUR : Propriété non définie dans le schéma
Message: "Validation error: Unexpected property 'filters' in tool 'search'"
Cause: Le schéma ne définit pas la propriété 'filters'
✅ SOLUTION : Définir explicitement toutes les propriétés
TOOLS_SCHEMA = {
"type": "function",
"function": {
"name": "search",
"description": "Recherche dans la base",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"minLength": 3
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 100
},
# Ajouter 'filters' AVANT d'utiliser l'outil
"filters": {
"type": "object",
"properties": {
"category": {"type": "string"},
"tags": {
"type": "array",
"items": {"type": "string"}
}
},
"additionalProperties": False # Bloquer d'autres props
}
},
"required": ["query"],
"additionalProperties": False # Strict mode
}
}
}
Pour MCP client HolySheep
client.register_tool(
name="search",
description="Recherche dans la base",
input_schema=TOOLS_SCHEMA["function"]["parameters"]
)Erreur 2 : Timeout sur les appels MCP avec latence élevée
# ❌ ERREUR : RequestTimeoutError après 30s
Message: "httpx.ReadTimeout: Attempt 3 of 3 failed"
Cause: Timeout trop court ou latence réseau élevée
✅ SOLUTION : Configurer timeouts adaptatifs et retry intelligent
import asyncio
from tenacity import retry, stop_after_attempt, wait_exponential
class AdaptiveTimeoutClient(HolySheepMCPClient):
"""Client avec timeouts adaptatifs."""
def __init__(self, *args, **kwargs):
self.base_timeout = kwargs.pop("timeout", 30.0)
super().__init__(*args, **kwargs)
# Timeout dynamique selon la complexité
self.timeout_rules = {
"simple": 5.0, # Requêtes directes
"medium": 15.0, # Avec une tool call
"complex": 45.0, # Multi-étapes
"batch": 120.0 # Lots de tokens
}
def _estimate_timeout(
self,
messages: List[Dict],
tools: Optional[List] = None
) -> float:
"""Estime le timeout nécessaire."""
# Calculer la taille estimée
total_chars = sum(
len(m.get("content", ""))
for m in messages
)
# Ajuster selon le contexte
if total_chars > 10000:
return self.timeout_rules["complex"]
elif tools or len(messages) > 5:
return self.timeout_rules["medium"]
elif total_chars > 5000:
return self.timeout_rules["medium"]
else:
return self.timeout_rules["simple"]
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=2, max=30)
)
async def chat_with_adaptive_timeout(
self,
messages: List[Dict],
tools: Optional[List] = None
):
"""Chat completion avec timeout adaptatif."""
estimated_timeout = self._estimate_timeout(messages, tools)
# Créer un client avec le bon timeout
adaptive_client = httpx.AsyncClient(
timeout=httpx.Timeout(estimated_timeout),
headers=self.client.headers.copy()
)
try:
response = await adaptive_client.post(
f"{self.BASE_URL}/chat/completions",
json={
"model": self.model,
"messages": messages,
"tools": tools
}
)
return response.json()
finally:
await adaptive_client.aclose()
# Exemple d'utilisation
async def demo():
client = AdaptiveTimeoutClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
model="deepseek-v3.2"
)
# Requête simple
result = await client.chat_with_adaptive_timeout([
{"role": "user", "content": "Hello!"}
])
# Requête complexe avec outils
result = await client.chat_with_adaptive_timeout(
messages=[{"role": "user", "content": "Analyse ce code..."}],
tools=TOOLS_SCHEMA
)Erreur 3 : Coûts explosifs à cause de boucles infinies de tool calls
# ❌ ERREUR : Coût de 150$ en quelques minutes
Symptôme: hundreds of tool_calls en quelques secondes
Cause: Le modèle appelle des outils en boucle sans condition d'arrêt
✅ SOLUTION : Implémenter des garde-fous et limites strictes
from dataclasses import dataclass
from typing import Optional
from datetime import datetime, timedelta
import threading
@dataclass
class CostGuard:
"""Garde-fou contre les coûts explosifs."""
max_calls_per_minute: int = 30
max_total_tokens: int = 100_000
max_tool_call_chain: int = 5 # Limite de chaines d'appels
max_cost_per_hour_usd: float = 10.0
_call_timestamps: list = None
_total_tokens: int = 0
_total_cost: float = 0.0
_tool_call_depth: int = 0
_lock: threading.Lock = None